1 Introduction

This study is not intended to give advice to state or local governments. This should be done by epidemiologists, who use sophisticated models to predict and advise.

This study is intended to give the general public and local governments a transparent and effective overview about the current state of COVID-19 for most states in the continental US and their ten most populous counties.

To understand the current situation the past needs to be referenced as a benchmark. Therefore, this study does not only presents the current values of Accumulated Cases, New Cases, and currently Active Cases, it also shows the time trend of these measures.

The study is based on an authoritative data source: the New York Times. For each of the counties and for the state as a whole, the Accumulated Cases and daily New Cases are extracted from this data base and reported similar to other publications (see Sections 1 and 2 for each county and the state as a whole).

However, focusing on Accumulated Cases is not effective, because this count includes cases of already recovered patients or patients that have died. Consequently, the focus of this study is on an estimate for the currently Active Cases (see Section 3 for each county and and the state as a whole).

The Active Cases reflect the true danger in the current situation, since New Cases are strongly correlated to Active Case. The more Active Cases there are, the greater the chances of new infections, leading to more New Cases.

A transparent and simple model is used to estimate Active Cases. The model is based on the assumption that it takes in average 14 days to recover from a COVID-19 infection. Estimates of medical experts vary between 10 and 20+ days therefore 14 days seems to be reasonable.

To predict the Active Cases for any day, the Active Cases of the previous day are used as a base. Then New Cases are added, and the recovered cases and deaths are subtracted.

Since data on recovered cases are not very reliable, New Cases from 14 days ago are used as a proxy for recovered cases and deaths. This is reasonable, since presumable all new patients from 14 days ago would be either recovered or dead by the current day.

Consequently, the predicted Active Cases for any day are calculated as the previous day’s Active Cases plus the New Cases from the current day minus the New Cases 14 days ago (estimate for recovered and deaths).

In order to get an idea about the trend of Active Cases, the daily growth rate is also provided (see Section 4 for each county and the state as a whole).

The study depends on currently available data. Since those are most likely underestimated, all findings of the study should also be considered as underestimated.

2 Oklahoma

2.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.¹ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

2.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.² This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

2.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.³ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

2.4 Daily %-Change in Activ Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.⁴ This makes it easier to see how the daily percentage growth rates developed over time.

2.5 Summary Charts

3 Oklahoma County

3.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.⁵ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

3.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.⁶ This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

3.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.⁷ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

3.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.⁸ This makes it easier to see how the daily percentage growth rates developed over time.

3.5 Summary Charts

4 Tulsa County

4.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.⁹ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

4.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.¹⁰ This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

4.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.¹¹ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

4.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.¹² This makes it easier to see how the daily percentage growth rates developed over time.

4.5 Summary Charts

5 Texas County

5.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.¹³ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

5.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.¹⁴ This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

5.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.¹⁵ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

5.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.¹⁶ This makes it easier to see how the daily percentage growth rates developed over time.

5.5 Summary Charts

6 Cleveland County

6.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.¹⁷ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

6.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.¹⁸ This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

6.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.¹⁹ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

6.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.²⁰ This makes it easier to see how the daily percentage growth rates developed over time.

6.5 Summary Charts

7 Washington County

7.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.²¹ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

7.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.²² This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

7.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.²³ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

7.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.²⁴ This makes it easier to see how the daily percentage growth rates developed over time.

7.5 Summary Charts

8 Comanche County

8.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.²⁵ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

8.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.²⁶ This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

8.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.²⁷ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

8.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.²⁸ This makes it easier to see how the daily percentage growth rates developed over time.

8.5 Summary Charts

9 Caddo County

9.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.²⁹ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

9.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.³⁰ This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

9.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.³¹ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

9.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.³² This makes it easier to see how the daily percentage growth rates developed over time.

9.5 Summary Charts

10 Wagoner County

10.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.³³ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

10.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.³⁴ This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

10.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.³⁵ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

10.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.³⁶ This makes it easier to see how the daily percentage growth rates developed over time.

10.5 Summary Charts

11 Canadian County

11.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.³⁷ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

11.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.³⁸ This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

11.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.³⁹ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

11.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.⁴⁰ This makes it easier to see how the daily percentage growth rates developed over time.

11.5 Summary Charts

12 McClain County

12.1 Accumulated Cases (Active and Not-Active)

The chart below shows the accumulation of all reported COVID-19 cases over time based on data from the New York Times.

To make the graph more readable, days with less than 10 Accumulated Cases are not reported.

Accumulated Cases include currently Active Cases, as well as recovered cases and deaths. Therefore, even when the pandemic is over, the curve of Accumulated Cases will not return to zero. Instead it will be entirely flat, but at a high level of cases. This makes it difficult to interpret the number of Accumulated Cases. Therefore, in Sections 3 and 4 are more suitable measure - the currently Active Cases will be introduced. The latter only includes infected patiens but neither recovered patients nor deaths.

In the chart below, the black line graph represents the actual Accumulated Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of Accumulated Cases.⁴¹ This makes it easier to see how the Accumulated Cases developed over time.

The grey area (standard error) around the blue line graph is an indicator for the quality of the smoothing estimate.

The slope of the black line graph represents the daily New Cases, which are also displayed in the chart in the following section.

12.2 Daily New Cases

New Cases reflect how many patients were registered for the first time as COVID-19 positive on a given day. When this measure approaches zero and after all (or most) of the patients have recovered, the COVID-19 crisis is over. New Cases are a good measure to determine how well the general public participates in social distancing.

In the chart below, the black line graph represents the actual New Cases as reported by the New York Times. The blue line graph shows a trend and smoothes eratic changes of New Cases.⁴² This makes it easier to see how the New Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

12.3 Predicted Active Cases

The prediction of Active Cases presents a crucial measure when evaluating the current COVID-19 situation. This is because every active case has the potential to infect even more people and thus can contribute to a more serious situation in the near future.

When the active cases decrease to a low level, then (and only then) the danger of future exponential growth of COVID-19 can be considered as small.

The model to predict the Active Cases is based on the assumption of a recovery time of 14 days. Estimates of medical experts vary between 10 and 20+ days. Therefore, 14 days seems to be reasonable. The Active Cases for any given day are predicted as follows:

Predicted Active Cases (previous day)

+ New Cases (current day)

- New Cases (14 days ago)

= Predicted Active Cases (current day)

New Cases (14 days ago) are used as an estimate for recovered cases and deaths, because under the recovery time assumption all people infected 14 days ago would have either recovered or died by today.

Note, all components of the above equation are written in stone as they are events from the distant past. The only exception are the New Cases. The latter component - the New Cases - is the only component that our society can control in the short run. New Cases stem from three different sources:

Very few New Cases stem from people who strictly obey the rules of social distancing.
New Cases also stem from the heroes of this pandemic such as supermarket workers, delivery personnel, and medical staff. New Cases from this source are mostly unavoidable.
Numerous New Cases stem from those who either don’t know better, consider the current situation as not serious, or are self-centered.

The third source is both high in numbers and certainly avoidable. Please, if you belong to the latter group, take a small sacrifice (even if you are not convinced) to potentially save lives and help to lower further damage to our economy.

In the chart below, the black line graph represents the Active Cases, predicted as explained above. The blue line graph shows a trend and smoothes eratic changes of these Active Cases over time.⁴³ This makes it easier to see how the Active Cases developed over time.

The grey area (standard error) around the blue graph is an indicator for the quality of the smoothing estimate.

12.4 Daily %-Change in Active Cases

The daily growth rate of Active Cases reflects our short-term success or failure. Even a small percentage increases will lead to catastrophic increases in active COVID-19 cases over a very short time. E.g., a daily increase of 5% will double the active cases after only two weeks.

In order to decrease the Active Cases to a sustainable level, it is not enough to reach a zero percentage change! Instead, a negative double digit percentage change over several days is needed.

In the chart below, the black line graph represents the daily growth rates. The blue line graph shows a trend and smoothes eratic changes of the daily growth rates over time.⁴⁴ This makes it easier to see how the daily percentage growth rates developed over time.

COVID-19 Total Cases and Estimates of Active Cases

C. Lange, Department of Economics, California State Polytechnic University, Pomona

30 August, 2023

1 Introduction

2 Oklahoma

2.1 Accumulated Cases (Active and Not-Active)

2.2 Daily New Cases

2.3 Predicted Active Cases

2.4 Daily %-Change in Activ Cases

2.5 Summary Charts

3 Oklahoma County

3.1 Accumulated Cases (Active and Not-Active)

3.2 Daily New Cases

3.3 Predicted Active Cases

3.4 Daily %-Change in Active Cases

3.5 Summary Charts

4 Tulsa County

4.1 Accumulated Cases (Active and Not-Active)

4.2 Daily New Cases

4.3 Predicted Active Cases

4.4 Daily %-Change in Active Cases

4.5 Summary Charts

5 Texas County

5.1 Accumulated Cases (Active and Not-Active)

5.2 Daily New Cases

5.3 Predicted Active Cases

5.4 Daily %-Change in Active Cases

5.5 Summary Charts

6 Cleveland County

6.1 Accumulated Cases (Active and Not-Active)

6.2 Daily New Cases

6.3 Predicted Active Cases

6.4 Daily %-Change in Active Cases

6.5 Summary Charts

7 Washington County

7.1 Accumulated Cases (Active and Not-Active)

7.2 Daily New Cases

7.3 Predicted Active Cases

7.4 Daily %-Change in Active Cases

7.5 Summary Charts

8 Comanche County

8.1 Accumulated Cases (Active and Not-Active)

8.2 Daily New Cases

8.3 Predicted Active Cases

8.4 Daily %-Change in Active Cases

8.5 Summary Charts

9 Caddo County

9.1 Accumulated Cases (Active and Not-Active)

9.2 Daily New Cases

9.3 Predicted Active Cases

9.4 Daily %-Change in Active Cases

9.5 Summary Charts

10 Wagoner County

10.1 Accumulated Cases (Active and Not-Active)

10.2 Daily New Cases

10.3 Predicted Active Cases

10.4 Daily %-Change in Active Cases

10.5 Summary Charts

11 Canadian County

11.1 Accumulated Cases (Active and Not-Active)

11.2 Daily New Cases

11.3 Predicted Active Cases

11.4 Daily %-Change in Active Cases

11.5 Summary Charts

12 McClain County

12.1 Accumulated Cases (Active and Not-Active)

12.2 Daily New Cases

12.3 Predicted Active Cases

12.4 Daily %-Change in Active Cases

12.5 Summary Charts